Investigating the use of portable X-ray fluorescence spectroscopy for rapid quantification of Phosphorus in wheat straw and grains

Gilbert Permalloo

CSIRO Root Architecture & Rhizosphere

Introduction

My name is Gilbert Permalloo. I am a Research Project Officer and I am presently working on roots architecture and rhizosphere of wheat. I worked in sugarcane agronomy for about 24 years and I was doing a little bit of basic programming in Fortran 77 and GWBasic about 30 years ago. Most of my data manipulation and visualisation are done in Excel. I could not write any code in R before I joined Data School and I was spending lots of time working with data in spreadsheets. On the otherhand, I am amazed to witness every day the marvel that R can do with data manipulation and visualisation.

My Project

The aim of this project is to investigate the use of portable X-Ray fluorescense spectrocopy (pXRF) as a rapid method to quantify the amount of phosphorus accumulated in straw and grains. Photo-1 shows pXRF used. About 200 grab samples were taken from one of three phosphorus trials (at 0kg and 30kg per hectare as treatments) for this study. The straw and grains were ground, and scanned by the pXRF. Two large datasets were generated by the pXRF; the chemistry dataset is composed of a wide range of chemical elemental composition quantified in ppm, whereas the beamspectra, are spectral values from three X-ray beams. R has been used to clean, tidy and re-organised the data, as well as for graphical visualisation of data. Data for phosphorus have been extracted from the large pXRF generated-dataset and merged with a dataframe that contains unique identification numbers (SampleID) that links the data to the sample source (STEM_ID) and other tables that contain agronomical data for each sample.

Preliminary results

Figures 1 and 2 show a relatively higher amount of phosphorus detected in the grains as compared to the straw. A slightly higher correlation between straw and grains for phosphorus assimilated under the 30kg/ha P treatment in comparison to the 0kg/ha P. Figure 2 shows that the level of P detected in the straw does not correlate with the grain yield. However, the amount of phosphorus detected in grains for the two treatments correlate differently to yield. Grains produced under the low treatment tend to show a slight positive correlation to yield as opposed to the 30kg/ha treatment, which has a negative correlation to yield.

This section will demonstrate the different visuals you might want use to show off your project. Don’t feel the need to go overboard, this is supposed to give a taste of the work you are doing rather than being a publication ready document.

Table 1: Amount of phosphorus (ppm) detected in straw and grains data
STEM_ID GENOTYPE SUBSAMPLE.x P in straw P level (kg/ha) P in grain yield
ST50PKT0WD9S CAV4081442 Straw 402 0 kg P 2699 1.802632
ST50PKT0WCYG CAV4080777 Straw 433 0 kg P 3587 2.854010
ST50PKT0WD1B CAV4081233 Straw 659 30 kg P 4627 3.201220
ST50PKT0WCD2 CAV4080976 Straw 334 0 kg P 3843 2.881579
ST50PKT0WC08 CAV4081051 Straw 430 30 kg P 4440 3.059210

Photo-1: pXRF instrument used to quantify amount of phosphorus in straw and grains

Plots from R

My Digital Toolbox

For this project, I have been using R - tidyverse, ggplot2, kableExtra, imager, data.table, readxl, lubridate

Favourite tools

My time went …

Most of my time went into tidying up and cleaning the data. Then I realised how crucial it is to understand the “how” and “what” data to be collected, and the structure and formatting - not to forget how and where they are stored. I wrote codes in R to resolve the issues and bring them together in one clean data set.

Next steps

What further steps do you wish your project could take? Or are there any new digital skills that you are keen to develop as a result of your involvement in the Data School?

My Data School Experience

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